library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(plotly)## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(ggplot2)Is there an association between the percentage of people who graduated high school and the incidence rate 1) across neighborhoods in NYC and 2) across neighborhoods in each borough?
data_clean <- read.csv("data_final.csv")
# List all column names
names(data_clean)## [1] "INCIDENT_KEY" "OCCUR_DATE"
## [3] "OCCUR_TIME" "BORO"
## [5] "LOC_OF_OCCUR_DESC" "PRECINCT"
## [7] "JURISDICTION_CODE" "LOC_CLASSFCTN_DESC"
## [9] "LOCATION_DESC" "STATISTICAL_MURDER_FLAG"
## [11] "PERP_AGE_GROUP" "PERP_SEX"
## [13] "PERP_RACE" "VIC_AGE_GROUP"
## [15] "VIC_SEX" "VIC_RACE"
## [17] "X_COORD_CD" "Y_COORD_CD"
## [19] "Latitude" "Longitude"
## [21] "Lon_Lat" "Neighborhood"
## [23] "neighbourhood_group" "NTA"
## [25] "Is_Holiday" "Year"
## [27] "Month" "OCCUR_DATETIME"
## [29] "Sky_Is_Dark" "NTAType"
## [31] "Total_population_nta" "CDTA"
## [33] "Number_poverty" "Percent_poverty"
## [35] "Number_education" "Percent_education"
## [37] "incident_rate_by_year_nta" "total_population_boro"
## [39] "incident_rate_by_year_boro"# Check the structure of the data frame
str(data_clean)## 'data.frame': 9820 obs. of 39 variables:
## $ INCIDENT_KEY : int 244608249 247542571 202853370 230311078 229224142 231246224 228559720 238210279 233431365 238238212 ...
## $ OCCUR_DATE : chr "2022-05-05" "2022-07-04" "2019-09-24" "2021-07-01" ...
## $ OCCUR_TIME : chr "00:10:00" "22:20:00" "21:00:00" "23:07:00" ...
## $ BORO : chr "MANHATTAN" "BRONX" "BRONX" "MANHATTAN" ...
## $ LOC_OF_OCCUR_DESC : chr "INSIDE" "OUTSIDE" NA NA ...
## $ PRECINCT : int 14 48 42 23 113 77 48 49 73 114 ...
## $ JURISDICTION_CODE : int 0 0 0 2 0 0 0 0 0 0 ...
## $ LOC_CLASSFCTN_DESC : chr "COMMERCIAL" "STREET" NA NA ...
## $ LOCATION_DESC : chr "VIDEO STORE" "(null)" NA "MULTI DWELL - PUBLIC HOUS" ...
## $ STATISTICAL_MURDER_FLAG : logi TRUE TRUE FALSE FALSE TRUE FALSE ...
## $ PERP_AGE_GROUP : chr "25-44" "(null)" "25-44" NA ...
## $ PERP_SEX : chr "M" "(null)" "M" NA ...
## $ PERP_RACE : chr "BLACK" "(null)" "UNKNOWN" NA ...
## $ VIC_AGE_GROUP : chr "25-44" "18-24" "25-44" "25-44" ...
## $ VIC_SEX : chr "M" "M" "M" "M" ...
## $ VIC_RACE : chr "BLACK" "BLACK" "BLACK" "BLACK" ...
## $ X_COORD_CD : num 986050 1016802 1014493 999061 1042534 ...
## $ Y_COORD_CD : num 214231 250581 242565 229912 184647 ...
## $ Latitude : num 40.8 40.9 40.8 40.8 40.7 ...
## $ Longitude : num -74 -73.9 -73.9 -73.9 -73.8 ...
## $ Lon_Lat : chr "POINT (-73.9935 40.754692)" "POINT (-73.88233 40.854402)" "POINT (-73.89071440599997 40.832416753000075)" "POINT (-73.94650786199998 40.79772716600007)" ...
## $ Neighborhood : chr "Hell's Kitchen" "Belmont" "East Morrisania" "East Harlem" ...
## $ neighbourhood_group : chr "Manhattan" "Bronx" "Bronx" "Manhattan" ...
## $ NTA : chr "Chelsea-Hudson Yards" "Belmont" "Crotona Park East" "East Harlem (North)" ...
## $ Is_Holiday : logi FALSE TRUE FALSE FALSE FALSE FALSE ...
## $ Year : int 2022 2022 2019 2021 2021 2021 2021 2021 2021 2021 ...
## $ Month : int 5 7 9 7 6 7 5 12 9 12 ...
## $ OCCUR_DATETIME : chr "2022-05-05 00:10:00" "2022-07-04 22:20:00" "2019-09-24 21:00:00" "2021-07-01 23:07:00" ...
## $ Sky_Is_Dark : logi TRUE TRUE TRUE TRUE FALSE TRUE ...
## $ NTAType : chr "Residential" "Residential" "Residential" "Residential" ...
## $ Total_population_nta : int 69741 35825 30158 64655 43090 15 35825 34623 37952 50225 ...
## $ CDTA : chr "MN 04" "BX 06" "BX 03" "MN 11" ...
## $ Number_poverty : chr "7,121" "12,919" "8,519" "20,588" ...
## $ Percent_poverty : num 11.5 39.6 29.4 32.3 11.4 NA 39.6 24.6 27.3 9.4 ...
## $ Number_education : chr "49,919" "12,455" "13,149" "34,290" ...
## $ Percent_education : num 94.6 65.6 69 75.9 83.5 NA 65.6 75.7 83.1 88.4 ...
## $ incident_rate_by_year_nta : num 0.0272 0.0809 0.0365 0.0619 0.0278 ...
## $ total_population_boro : int 18415085 22955825 11660890 20085354 13629328 38087730 29778638 29778638 38087730 13629328 ...
## $ incident_rate_by_year_boro: num 0.00167 0.00233 0.00229 0.00171 0.00217 ...# Step 1: Check the structure of your dataset to ensure the necessary columns exist
head(data_clean)## INCIDENT_KEY OCCUR_DATE OCCUR_TIME BORO LOC_OF_OCCUR_DESC PRECINCT
## 1 244608249 2022-05-05 00:10:00 MANHATTAN INSIDE 14
## 2 247542571 2022-07-04 22:20:00 BRONX OUTSIDE 48
## 3 202853370 2019-09-24 21:00:00 BRONX <NA> 42
## 4 230311078 2021-07-01 23:07:00 MANHATTAN <NA> 23
## 5 229224142 2021-06-07 19:55:00 QUEENS <NA> 113
## 6 231246224 2021-07-22 01:47:00 BROOKLYN <NA> 77
## JURISDICTION_CODE LOC_CLASSFCTN_DESC LOCATION_DESC
## 1 0 COMMERCIAL VIDEO STORE
## 2 0 STREET (null)
## 3 0 <NA> <NA>
## 4 2 <NA> MULTI DWELL - PUBLIC HOUS
## 5 0 <NA> <NA>
## 6 0 <NA> MULTI DWELL - APT BUILD
## STATISTICAL_MURDER_FLAG PERP_AGE_GROUP PERP_SEX PERP_RACE VIC_AGE_GROUP
## 1 TRUE 25-44 M BLACK 25-44
## 2 TRUE (null) (null) (null) 18-24
## 3 FALSE 25-44 M UNKNOWN 25-44
## 4 FALSE <NA> <NA> <NA> 25-44
## 5 TRUE <NA> <NA> <NA> 45-64
## 6 FALSE <NA> <NA> <NA> 25-44
## VIC_SEX VIC_RACE X_COORD_CD Y_COORD_CD Latitude Longitude
## 1 M BLACK 986050 214231 40.75469 -73.99350
## 2 M BLACK 1016802 250581 40.85440 -73.88233
## 3 M BLACK 1014493 242565 40.83242 -73.89071
## 4 M BLACK 999061 229912 40.79773 -73.94651
## 5 M BLACK 1042534 184647 40.67331 -73.78989
## 6 M BLACK 1004507 182865 40.66858 -73.92698
## Lon_Lat Neighborhood
## 1 POINT (-73.9935 40.754692) Hell's Kitchen
## 2 POINT (-73.88233 40.854402) Belmont
## 3 POINT (-73.89071440599997 40.832416753000075) East Morrisania
## 4 POINT (-73.94650786199998 40.79772716600007) East Harlem
## 5 POINT (-73.78988688199998 40.673306465000046) Jamaica
## 6 POINT (-73.92697993199994 40.66858395700007) Crown Heights
## neighbourhood_group NTA Is_Holiday Year Month
## 1 Manhattan Chelsea-Hudson Yards FALSE 2022 5
## 2 Bronx Belmont TRUE 2022 7
## 3 Bronx Crotona Park East FALSE 2019 9
## 4 Manhattan East Harlem (North) FALSE 2021 7
## 5 Queens Baisley Park FALSE 2021 6
## 6 Brooklyn Lincoln Terrace Park FALSE 2021 7
## OCCUR_DATETIME Sky_Is_Dark NTAType Total_population_nta CDTA
## 1 2022-05-05 00:10:00 TRUE Residential 69741 MN 04
## 2 2022-07-04 22:20:00 TRUE Residential 35825 BX 06
## 3 2019-09-24 21:00:00 TRUE Residential 30158 BX 03
## 4 2021-07-01 23:07:00 TRUE Residential 64655 MN 11
## 5 2021-06-07 19:55:00 FALSE Residential 43090 QN 12
## 6 2021-07-22 01:47:00 TRUE Park 15 BK 08
## Number_poverty Percent_poverty Number_education Percent_education
## 1 7,121 11.5 49,919 94.6
## 2 12,919 39.6 12,455 65.6
## 3 8,519 29.4 13,149 69.0
## 4 20,588 32.3 34,290 75.9
## 5 5,054 11.4 25,416 83.5
## 6 <NA> NA <NA> NA
## incident_rate_by_year_nta total_population_boro incident_rate_by_year_boro
## 1 0.02724366 18415085 0.001667112
## 2 0.08094906 22955825 0.002330563
## 3 0.03647457 11660890 0.002289705
## 4 0.06186683 20085354 0.001707712
## 5 0.02784869 13629328 0.002171787
## 6 13.33333333 38087730 0.001656702# Calculate the correlation between the poverty percentage and the incident rate
correlation <- cor(data_clean$incident_rate_by_year_nta, data_clean$Percent_education, use = "complete.obs")
print(paste("Correlation coefficient: ", correlation))## [1] "Correlation coefficient: -0.274782643621429"# Create a scatter plot to visualize the relationship
data_clean %>%
plot_ly(x = ~Percent_education, y = ~incident_rate_by_year_nta,
color = ~NTA, colors = "viridis",
type = "scatter", mode = "markers",
text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", BORO,
"<br>% graduated HS: ", Percent_education,
"<br>Incident Rate: ", incident_rate_by_year_nta)) %>%
layout(title = "Percent graduated high school and Incident Rate in NYC",
xaxis = list(title = 'Percentage of People graduated in high school'),
yaxis = list(title = 'Incident Rate'),
legend = list(title = list(text = 'Neighborhood')))## Warning: Ignoring 95 observations# Assuming your main data frame is named 'data_clean'
# Filter data for Manhattan
manhattan_data <- data_clean %>%
filter(neighbourhood_group == "Manhattan")
# Filter data for Brooklyn
brooklyn_data <- data_clean %>%
filter(neighbourhood_group == "Brooklyn")
# Filter data for The Bronx
bronx_data <- data_clean %>%
filter(neighbourhood_group == "Bronx")
# Filter data for Staten Island
staten_island_data <- data_clean %>%
filter(neighbourhood_group == "Staten Island")
# Filter data for Queens
queens_data <- data_clean %>%
filter(neighbourhood_group == "Queens")# Function to compute and print correlation
compute_correlation <- function(data, borough_name) {
correlation <- cor(
data$incident_rate_by_year_nta,
data$Percent_education,
use = "complete.obs"
)
cat("Correlation coefficient for", borough_name, ":", correlation, "\n")
}
compute_correlation(manhattan_data, "Manhattan")## Correlation coefficient for Manhattan : -0.2284435compute_correlation(brooklyn_data, "Brooklyn")## Correlation coefficient for Brooklyn : -0.248732compute_correlation(bronx_data, "Bronx")## Correlation coefficient for Bronx : -0.3996457compute_correlation(staten_island_data, "Staten Island")## Correlation coefficient for Staten Island : -0.6452131compute_correlation(queens_data, "Queens")## Correlation coefficient for Queens : 0.1006569compute_correlation(manhattan_data, "Manhattan")## Correlation coefficient for Manhattan : -0.2284435# Function to create scatter plot with trend line
# Scatter plot for Manhattan
data_clean |>
filter(neighbourhood_group == "Manhattan") |>
plot_ly(data = _, x = ~Percent_education, y = ~incident_rate_by_year_nta,
color = ~NTA,
colors = "viridis",
type = "scatter",
mode = "markers",
text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", neighbourhood_group,
"<br>% graduated HS: ", Percent_education,
"<br>Incident Rate: ", incident_rate_by_year_nta)) |>
layout(title = "Percent graduated high school and Incident Rate in Manhattan",
xaxis = list(title = 'Percentage of People graduated in high school'),
yaxis = list(title = 'Incident Rate'),
legend = list(title = list(text = 'Neighborhood')))## Warning: Ignoring 6 observations# Scatter plot for Brooklyn
data_clean |>
filter(neighbourhood_group == "Brooklyn") |>
plot_ly(data = _, x = ~Percent_poverty, y = ~incident_rate_by_year_nta,
color = ~NTA,
colors = "plasma",
type = "scatter",
mode = "markers",
text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", neighbourhood_group,
"<br>% graduated HS: ", Percent_education,
"<br>Incident Rate: ", incident_rate_by_year_nta)) |>
layout(title = "Percent graduated high school and Incident Rate in Brooklyn",
xaxis = list(title = 'Percentage of People graduated in high school'),
yaxis = list(title = 'Incident Rate'),
legend = list(title = list(text = 'Neighborhood')))## Warning: Ignoring 13 observations# Scatter plot for The Bronx
data_clean |>
filter(neighbourhood_group == "Bronx") |>
plot_ly(data = _, x = ~Percent_poverty, y = ~incident_rate_by_year_nta,
color = ~NTA,
colors = "magma",
type = "scatter",
mode = "markers",
text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", neighbourhood_group,
"<br>% graduated HS: ", Percent_education,
"<br>Incident Rate: ", incident_rate_by_year_nta)) |>
layout(title = "Percent graduated high school and Incident Rate in The Bronx",
xaxis = list(title = 'Percentage of People graduated in high school'),
yaxis = list(title = 'Incident Rate'),
legend = list(title = list(text = 'Neighborhood')))## Warning: Ignoring 25 observations# Scatter plot for Queens
data_clean |>
filter(neighbourhood_group == "Queens") |>
plot_ly(data = _, x = ~Percent_poverty, y = ~incident_rate_by_year_nta,
color = ~NTA,
colors = "inferno",
type = "scatter",
mode = "markers",
text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", neighbourhood_group,
"<br>% graduated HS: ", Percent_education,
"<br>Incident Rate: ", incident_rate_by_year_nta)) |>
layout(title = "Percent graduated high school and Incident Rate in Queens",
xaxis = list(title = 'Percentage of People graduated in high school'),
yaxis = list(title = 'Incident Rate'),
legend = list(title = list(text = 'Neighborhood')))## Warning: Ignoring 11 observations# Scatter plot for Staten Island
data_clean |>
filter(neighbourhood_group == "Staten Island") |>
plot_ly(data = _, x = ~Percent_poverty, y = ~incident_rate_by_year_nta,
color = ~NTA,
colors = "inferno",
type = "scatter",
mode = "markers",
text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", neighbourhood_group,
"<br>% graduated HS: ", Percent_education,
"<br>Incident Rate: ", incident_rate_by_year_nta)) |>
layout(title = "Percent graduated high school and Incident Rate in Queens",
xaxis = list(title = 'Percentage of People graduated in Staten Island'),
yaxis = list(title = 'Incident Rate'),
legend = list(title = list(text = 'Neighborhood')))## Warning: Ignoring 5 observations